Biometrics is a science that can be used to measure and analyze physiological characteristics, such as fingerprints, eye retinas and irises, facial patterns and hand geometry. Some biometrics technologies involve measurement and analysis of behavioral characteristics, such as voice patterns, signatures, and typing patterns. Because biometrics, especially physiological-based technologies, measures qualities that an individual usually cannot change, it can be especially effective for authentication and identification purposes.
Fingerprint-based identification is one of the oldest successful biometric-based identification methods. Each person has a set of unique, typically immutable fingerprints. A fingerprint includes a series of ridges and valleys (or “furrows”) on the surface of a finger. The uniqueness of a fingerprint can be determined by a pattern of ridges and furrows, as well as minutiae points. Minutiae points are local ridge characteristics that generally occur at either a ridge bifurcation or at a ridge ending.
Fingerprint matching techniques can be placed into two general categories: minutiae-based and correlation-based matching. Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. Each minutiae point may include a placement location (e.g., an x, y coordinate in an image or spatial domain) and a directional angle. (The curious reader is directed to, e.g., U.S. Pat. Nos. 3,859,633 and 3,893,080, both to Ho et al., which discuss fingerprint identification based upon fingerprint minutiae matching. Each of these patent documents is herein incorporated by reference.) The National Institute of Standards and Technology (NIST) distributes public domain software for fingerprint analysis. The software is available from the Image Group at NIST under the name NIST FINGERPRINT IMAGE SOFTWARE (NFIS), which includes a minutiae detector called, MINDTCT. MINDTCT automatically locates and records ridge ending and bifurcations in a fingerprint image (e.g., identifies minutiae locations). NFIS also includes a pattern classification module called PCASYS.
Correlation techniques correlate normalized versions of fingerprint images to determine if a first fingerprint image (control) matches a second fingerprint image (sample). (The curious reader is direction to, e.g., U.S. Pat. Nos. 6,134,340 and 5,067,162, which discuss correlation techniques even further. Each of these patent documents is herein incorporated by reference.).
Other fingerprinting efforts have focused on locating or analyzing the so-called fingerprint “core”. U.S. Pat. No. 5,040,224 to Hara discloses an approach for preprocessing fingerprints to correctly determine a position of a core of each fingerprint image for later matching by minutiae patterns. U.S. Pat. No. 5,140,642 to Hsu et al. is directed to a method for determining the actual position of a core point of a fingerprint based upon finding ridge flows and assigning a direction code, correcting the ridge flows, and allocating the core point based upon the corrected direction codes. Each of these patents is herein incorporated by reference.
Despite the work in the prior art, there are still problems to be solved, and improvements to be made. For example, quality of an original fingerprint image can be poor—due to imaging issues or physical conditions (e.g., wetness, dryness, etc.) when sampling a fingerprint. When fingerprint quality is poor, the print may contain local ridge pattern distortion, which may result in an incorrect analysis of the fingerprint.
Accordingly, one inventive aspect of the invention provides a method to assess the quality of fingerprint images using local statistics of a captured fingerprint. Assessing the quality of a fingerprint is vital, e.g., to determine whether a fingerprint should be recaptured or whether a fingerprint image should be modified or enhanced.
This disclosure also includes systems and methods for hiding fingerprint minutiae information in a photographic image (e.g., a photograph carried by an ID document). The fingerprint minutiae information is represented as a so-called digital watermark component.
Digital watermarking is a process for modifying physical media or electronic signals to embed a machine-readable code into the media or signals. The media or signals may be modified such that the embedded code is imperceptible or nearly imperceptible to the user, yet may be detected through an automated detection process.
Digital watermarking systems typically have two primary components: an encoder that embeds the watermark in a host signal, and a decoder that detects and reads the embedded watermark from a signal suspected of containing a watermark (a suspect signal). The encoder embeds a watermark by altering the host signal. The reading component analyzes a suspect signal to detect whether a watermark is present. In applications where the watermark encodes information, the reader extracts this information from the detected watermark. Several particular watermarking techniques have been developed. The reader is presumed to be familiar with the literature in this field. Some techniques for embedding and detecting imperceptible watermarks in media signals are detailed in assignee's U.S. Pat. Nos. 5,862,260 and 6,614,914. Each of these patent documents is herein incorporated by reference.
Further features and advantages will become even more apparent with reference to the following detailed description and accompanying drawings.
Appendix A FIGS. 1a-d illustrate, respectively, a fingerprint image, a global directional map, a block orientation and a measured thickness.
Appendix A FIGS. 2a-2c illustrate for a good fingerprint, respectively, a fingerprint image, a rotated black/white image, and a Gaussian PDF of line thickness.
Appendix A FIGS. 3a-3c illustrate for a wet fingerprint, respectively, a fingerprint image, a rotated black/white image and a Gaussian PDF of line thickness.
Appendix A FIGS. 4a-4c illustrate for a dry fingerprint, respectively, a fingerprint image, a rotated black/white image, and a Gaussian PDF of line thickness.
Appendix A FIGS. 5a-5c illustrate, respectively, good, dry and wet fingerprints, while Appendix A FIGS. 5d-5f illustrates, respectively, detected poor quality blocks for the fingerprints shown in FIGS. 5a-5c. 
Appendix A FIGS. 6a-6d illustrates detected dray blocks before and after morphology; specifically, FIG. 6a shows a fingerprint with dry blocks without morphology; FIG. 6b shows a number of dry block in the FIG. 6a fingerprint after morphology; FIG. 6c shows a close up of a dry block; and FIG. 6d shows a close up of the FIG. 6c block after morphology.
Appendix A FIGS. 7a-7b show a Dry index and a corresponding Cluster Index.
Appendix A FIGS. 8a-8c illustrate, respectively, a fingerprint image, the number of detected dry blocks within the image, and a group of dry blocks marked with a square.
Appendix A FIGS. 9a-9c illustrate, respectively, a fingerprint image, the number of detected dry blocks and a group of dry blocks marked with a squares.